ABSTRACT
Analysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here we introduce BrainStat - a toolbox for (i) univariate and multivariate general linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, as well as task-based fMRI meta-analysis, canonical resting-state fMRI networks, and resting-state derived gradients across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox offers to streamline analytical processes and accelerate cross-modal. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation.
Competing Interest Statement
The authors have declared no competing interest.
